Annual State-reported licensed driver data from Highway Statistics for the 50 States and DC from Highway Statistics table DL-22.

United States Average Vehicles per Household: 4 or More Licensed Drivers data was reported at 4.100 Unit in 2017. This records an increase from the previous number of 3.900 Unit for 2009. United States Average Vehicles per Household: 4 or More Licensed Drivers data is updated yearly, averaging 3.850 Unit from Dec 1991 (Median) to 2017, with 4 observations. The data reached an all-time high of 4.100 Unit in 2017 and a record low of 3.800 Unit in 2001. United States Average Vehicles per Household: 4 or More Licensed Drivers data remains active status in CEIC and is reported by Center for Transportation Analysis. The data is categorized under Global Database’s United States – Table US.TA003: Number of Vehicles per Household.

This study focuses on the drinking and driving habits of Americans. The questionnaire contained 51 questions. Respondents were interviewed over the telephone and asked about their frequency of consumption of alcoholic beverages, where they most often drank, their mode of transportation to and from this location, their driving and drinking experiences, and their age, sex, educational attainment, and socioeconomic status.

United States Average Vehicles per Household: 2 Licensed Drivers data was reported at 2.200 Unit in 2009. This stayed constant from the previous number of 2.200 Unit for 2001. United States Average Vehicles per Household: 2 Licensed Drivers data is updated yearly, averaging 2.200 Unit from Dec 1991 (Median) to 2009, with 3 observations. The data reached an all-time high of 2.200 Unit in 2009 and a record low of 2.100 Unit in 1991. United States Average Vehicles per Household: 2 Licensed Drivers data remains active status in CEIC and is reported by Center for Transportation Analysis. The data is categorized under Global Database’s USA – Table US.TA003: Number of Vehicles per Household.

United States Average Vehicles per Household: 3 Licensed Drivers data was reported at 3.000 Unit in 2009. This stayed constant from the previous number of 3.000 Unit for 2001. United States Average Vehicles per Household: 3 Licensed Drivers data is updated yearly, averaging 3.000 Unit from Dec 1991 (Median) to 2009, with 3 observations. The data reached an all-time high of 3.000 Unit in 2009 and a record low of 2.900 Unit in 1991. United States Average Vehicles per Household: 3 Licensed Drivers data remains active status in CEIC and is reported by Center for Transportation Analysis. The data is categorized under Global Database’s USA – Table US.TA003: Number of Vehicles per Household.

Comprehensive dataset of 1 Drivers license training schools in North Dakota, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

United States Ave Vehicle Miles Traveled per Household: 3 Licensed Drivers data was reported at 37,700.000 Mile in 2009. This records a decrease from the previous number of 37,900.000 Mile for 2001. United States Ave Vehicle Miles Traveled per Household: 3 Licensed Drivers data is updated yearly, averaging 37,700.000 Mile from Dec 1991 (Median) to 2009, with 3 observations. The data reached an all-time high of 37,900.000 Mile in 2001 and a record low of 29,400.000 Mile in 1991. United States Ave Vehicle Miles Traveled per Household: 3 Licensed Drivers data remains active status in CEIC and is reported by Center for Transportation Analysis. The data is categorized under Global Database’s USA – Table US.TA005: Vehicles Miles Traveled per Household.

This is a list of drivers with a current TLC Driver License, which authorizes drivers to operate NYC TLC licensed yellow and green taxicabs and for-hire vehicles (FHVs). This list is accurate as of the date and time shown in the Last Date Updated and Last Time Updated fields. Questions about the contents of this dataset can be sent by email to: licensinginquiries@tlc.nyc.gov.

United States Average Vehicles per Household: 1 Licensed Driver data was reported at 1.100 Unit in 2009. This records a decrease from the previous number of 1.200 Unit for 2001. United States Average Vehicles per Household: 1 Licensed Driver data is updated yearly, averaging 1.200 Unit from Dec 1991 (Median) to 2009, with 3 observations. The data reached an all-time high of 1.500 Unit in 1991 and a record low of 1.100 Unit in 2009. United States Average Vehicles per Household: 1 Licensed Driver data remains active status in CEIC and is reported by Center for Transportation Analysis. The data is categorized under Global Database’s USA – Table US.TA003: Number of Vehicles per Household.

Comprehensive dataset of 11 Drivers license training schools in Minnesota, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This dataset provides information on motor vehicle operators (drivers) involved in traffic collisions occurring on county and local roadways. The dataset reports details of all traffic collisions occurring on county and local roadways within Montgomery County, as collected via the Automated Crash Reporting System (ACRS) of the Maryland State Police, and reported by the Montgomery County Police, Gaithersburg Police, Rockville Police, or the Maryland-National Capital Park Police. This dataset shows each collision data recorded and the drivers involved.

Please note that these collision reports are based on preliminary information supplied to the Police Department by the reporting parties. Therefore, the collision data available on this web page may reflect:

-Information not yet verified by further investigation -Information that may include verified and unverified collision data -Preliminary collision classifications may be changed at a later date based upon further investigation -Information may include mechanical or human error

This dataset can be joined with the other 2 Crash Reporting datasets (see URLs below) by the State Report Number. * Crash Reporting - Incidents Data at https://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Incidents-Data/bhju-22kf * Crash Reporting - Non-Motorists Data at https://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Non-Motorists-Data/n7fk-dce5

Update Frequency : Weekly

The survey will explore the frequency and context of unsafe driver behaviors among this age group, types of risky situations that people of young driver age experience as vehicle passengers, parental influence on young drivers, driver training and education, and young driver attitudes and perceptions concerning selected traffic safety issues.

United States Ave Vehicle Miles Traveled per Household: 2 Licensed Drivers data was reported at 23,500.000 Mile in 2009. This records a decrease from the previous number of 25,800.000 Mile for 2001. United States Ave Vehicle Miles Traveled per Household: 2 Licensed Drivers data is updated yearly, averaging 23,500.000 Mile from Dec 1991 (Median) to 2009, with 3 observations. The data reached an all-time high of 25,800.000 Mile in 2001 and a record low of 22,900.000 Mile in 1991. United States Ave Vehicle Miles Traveled per Household: 2 Licensed Drivers data remains active status in CEIC and is reported by Center for Transportation Analysis. The data is categorized under Global Database’s USA – Table US.TA005: Vehicles Miles Traveled per Household.

The NexusStreets dataset contains human and autonomous driving scenes. They are collected by monitoring a target vehicle that can be either autonomous or controlled by a human driver. Data is presented in the shape of:

sequences of JPEG images, one image per timestamp

target vehicle state information for each timestamp

The dataset has been built on the CARLA simulator, thanks to Baidu Apollo and a Logitech G29 steering wheel for the autonomous and human drivings, respectively.The dataset consists of 520 scenes (260 pairs of mirrored scenarios) of 60 seconds each.The folders are organized as follows:

. ├── ... ├──
│ ├──
│ │ ├──
│ │ │ └── ...
│ │ └── ... │ └── ... └── ...

driving mode: corresponds to the control modality of the target vehicle under test and can be either Baidu Apollo or manual driving;

town: one of the five default maps in CARLA (e.g., Town01, Town02, etc);

trial: 60 different trials per map, they differ in traffic and weather conditions (except Town04). Each trial records 60 seconds of simulation, logging 120 frames per video and an equal number of rows per CSV. In particular, each trial includes:

video: this folder groups the JPEG images;

state_features.csv: reports the state information of the target vehicle for each frame;

detection_features.csv: reports the 2D bounding box detections obtained from a pre-trained YOLOv7 detector.

Introduction

Welcome to the US English Language In-car Speech Dataset, a comprehensive collection of audio recordings designed to facilitate the development of speech recognition models specifically tailored for in-car environments. This dataset aims to support research and innovation in automotive speech technology, enabling seamless and robust voice interactions within vehicles for drivers and co-passengers.

Speech Data

This dataset comprises over 5,000 high-quality audio recordings collected from various in-car environments. These recordings include scripted wake words and command-type prompts.

Dataset Diversity

Apart from participant diversity, the dataset is diverse in terms of different wake words, voice commands, and recording environments.

Metadata

The dataset provides comprehensive metadata for each audio recording and participant:

Sample Data: https://cloud.drivertechnologies.com/shared?s=146&t=4:03&token=0f469c88-d578-4b4f-80b2-f53f195683b2

At Driver Technologies, we are dedicated to harnessing advanced technology to gather anonymized critical driving data through our innovative dash cam app, which operates seamlessly on end users' smartphones. Our Speed Over Limit Driver Behavior Data offering is a key resource for understanding driver behavior and improving safety on the roads, making it an essential tool for various industries.

What Makes Our Data Unique? Our Speed Over Limit Driver Behavior Data is distinguished by its real-time collection capabilities, utilizing our built-in computer vision technology to identify and capture instances where a driver nearly gets into an accident. This data reflects critical safety events that are indicative of potential risks and non-compliance with traffic regulations. By providing data on these significant events, our dataset empowers clients to perform in-depth analysis.

How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios. For our Speed Over Limit Driver Behavior Data, we leverage computer vision models to read speed limit signs as the driver drives past them, then compare that to speed data captured using the phone's sensor.

Primary Use-Cases and Verticals Driver Behavior Analysis: Organizations can leverage our dataset to analyze driving habits and identify trends in driver behavior. This analysis can help in understanding patterns related to rule compliance and potential risk factors.

Training Computer Vision Models: Clients can utilize our annotated data to develop and refine their own computer vision models for applications in autonomous vehicles, ensuring better decision-making capabilities in complex driving environments.

Improving Risk Assessment: Insurers can utilize our dataset to refine their risk assessment models. By understanding the frequency and context of significant events, they can better evaluate driver risk profiles, leading to more accurate premium pricing and improved underwriting processes.

Integration with Our Broader Data Offering The Speed Over Limit Driver Behavior Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and smart city planning.

In summary, Driver Technologies' Speed Over Limit Driver Behavior Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Speed Over Limit Driver Behavior Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.

Graph and download economic data for Moving 12-Month Total Vehicle Miles Traveled (M12MTVUSM227NFWA) from Dec 1970 to Apr 2025 about miles, travel, vehicles, and USA.

Historical licensee status data. Data dictionary available here: https://data.cityofnewyork.us/api/views/p32s-yqxq/files/20bca1de-74f0-462e-8574-f25ca66b8346?download=true&filename=NewDriverAppStatusLookupLegend.pdf

For questions, please email newdriverapp@tlc.nyc.gov.

The overall objective of the i-DREAMS project is to setup a framework for the definition, development, testing and validation of a context-aware safety envelope for driving (‘Safety Tolerance Zone’), within a smart Driver, Vehicle & Environment Assessment and Monitoring System (i-DREAMS). Taking into account driver background factors and real-time risk indicators associated with the driving performance as well as the driver state and driving task complexity indicators, a continuous real-time assessment is made to monitor and determine if a driver is within acceptable boundaries of safe operation. Moreover, safety-oriented interventions were developed to inform or warn the driver real-time in an effective way as well as on an aggregated level after driving through an app- and web-based gamified coaching platform. The conceptual framework, which was tested in a simulator study and three stages of on-road trials in Belgium, Germany, Greece, Portugal and the United Kingdom on a total of 600 participants representing car, bus, and truck drivers, respectively. Specifically, the Safety Tolerance Zone (STZ) is subdivided into three phases, i.e. ‘Normal driving phase’, the ‘Danger phase’, and the ‘Avoidable accident phase’. For the real-time determination of this STZ, the monitoring module in the i-DREAMS platform continuously register and process data for all the variables related to the context and to the vehicle. Regarding the operator, however, continuous data registration and processing are limited to mental state and behavior. Finally, it is worth mentioning that data related to operator competence, personality, socio-demographic background, and health status, are collected via survey questionnaires. More information of the project can be seen from project website: https://idreamsproject.eu/wp/

This dataset contains naturalistic driving data of various trips of participants recruited in i-Dreams project. Various different types of events are recorded for different intensity levels such as headway, speed, acceleration, braking, cornering, fatigue and illegal overtaking. Running headway, speed, distance, wipers use, handheld phone use, high beam use and other data is also recorded. Driver characteristics are also available but not part of this sample data. In the i-Dreams project, raw data for a particular trip was collected via CardioID gateway, Mobileye, wristband or CardioWheel. These trip data are fused using a feature-based data fusion technique, namely geolocation through synchronization and support vector machines. The system provided by CardioID integrates several data streams, generated by the different sensors that make up the inputs of the i-Dreams system. The sample dataset is fused, processed as well as aggregated to produce consistent time series data of trips for a particular time interval such as 30 secs/ 60 secs or 2- minutes intervals. More datasets can be acquired for analysis purposes by following the data acquisition process given in the data description file.

Motivation

Drowsiness is an intermediate condition that fluctuates between alertness and sleep. It reduces the consciousness level andhinders a person from responding quickly to important road safety issues [1]. The American Automobile Association (AAA) has reported that about 24% of 2,714 drivers that participated in a survey revealed being extremely drowsy while driving, at least once in the last month [2]. In 2017, the National Highway Transportation Safety Administration (NHTSA) also reported 795 fatalities in motor vehicle crashes involving drowsy drivers [3]. Drowsy driving has caused about 2.5% of fatal accidents from 2011 through 2015 in the USA, and it is estimated to produce an economic loss of USD 230 billion annually [4]. Klauer et al. have found in their study that drowsy drivers contributed to 22-24% of crashes or near-crash risks [5]. The German Road Safety Council (DVR) has reported that one out of four fatal highway crashes has been caused by drowsy drivers [6]. In a study carried out in 2015, it has been reported that the average prevalence of falling asleep while driving in the previous two years was about 17% in 19 European countries [6]. The results of these studies emphasize the importance of detecting drowsiness early enough to initiate preventive measures. Drowsiness detection systems are intended to warn the drivers before an upcoming level of drowsiness gets critical to prevent drowsiness-related accidents.

Intelligent Systems that automate motor vehicle driving on the roads are being introduced to the market step-wise. The Society of Automotive Engineers (SAE) issued a standard defining six levels ranging from no driving automation (level 0) to full driving automation (level 5) [7]. While the SAE levels 0-2 require that an attentive driver carries out or at least monitors the dynamic driving task, in the SAE level 3 of automated driving, drivers will be allowed to do a secondary task allowing the system to control the vehicle under limited conditions, e.g., on a motorway. Still, the automation system has to hand back the vehicle guidance to the driver whenever it cannot control the state of the vehicle any more. However, the handover of vehicle control to a drowsy driver is not safe. Therefore, the system should be informed about the state of the driver.

To date, different Advanced Driver Assistance Systems (ADAS) have been made by car manufactures and researchers to improve driving safety and manage the traffic flow. ADAS systems have been benefited from advanced machine perception methods, improved computing hardware systems, and intelligent vehicle control algorithms. By recently increasing the availability of huge amounts of sensor data to ADAS, data-driven approaches are extensively exploited to enhance their performance. The driver drowsiness detection systems have gained much attention from researchers. Before its use in the development of driving automation, drowsiness warning systems have been produced for the direct benefit of avoiding accidents.

The aim of the WACHSens project was to collect a big data set to detect the different levels of driver drowsiness during performing two different driving modes: manual and automated.

To retrieve this data set, please send a request to: arno.eichberger@tugraz.at

References:

[1] M. Awais, N. Badruddin, and M. Drieberg, "A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability,"Sensors, vol. 17, no. 9, 2017, doi: 10.3390/s17091991

[2] AAA Foundation for Traffic Safety, "2019 Traffic Safety Culture Index (Technical Report), June 2020," Washington, D.C., Jun. 2020. [Online]. Available: https://aaafoundation.org/2019-traffic-safety-culture-index/

[3] National Highway Traffic Safety Administration, "Traffic Safety Facts: 2017 Fatal Motor Vehicle Crashes: Overview," NHTSA's National Center for Statistics and Analysis, 1200 New Jersey Avenue SE., Washington DOT HS 812 603, Oct. 2018. Accessed: Apr. 14 2021. [Online]. Available: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812603

[4] Agustina Garcés Correa, Lorena Orosco, and Eric Laciar, "Automatic detection of drowsiness in EEG records based on multimodal analysis," Medical Engineering & Physics, vol. 36, no. 2, pp. 244–249, 2014, doi: 10.1016/j.medengphy.2013.07.011

[5] S. Klauer, V. Neale, T. Dingus, Jeremy Sudweeks, and D. J. Ramsey, "The Prevalence of Driver Fatigue in an Urban Driving Environment : Results from the 100-Car Naturalistic Driving Study," in 2006.

[6] Fraunhofer-Gesellschaft,Eyetracker warns against momentary driver drowsiness - Press Release Oktober 12, 2010. [Online]. Available: https://www.fraunhofer.de/en/press/research-news/2010/10/eye-tracker-driver-drowsiness.html (accessed: Apr. 14 2021).

[7] T. Inagaki and T. B. Sheridan, "A critique of the SAE conditional driving automation definition, and analyses of options for improvement," Cogn Tech Work, vol. 21, no. 4, pp. 569–578, 2019, doi: 10.1007/s10111-018-0471-5